Methods and apparatus for unknown speaker labeling using concurrent speech recognition, segmentation, classification and clustering

ABSTRACT

A method and apparatus are disclosed for identifying speakers participating in an audio-video source, whether or not such speakers have been previously registered or enrolled. The speaker identification system uses an enrolled speaker database that includes background models for unenrolled speakers, such as “unenrolled male” or “unenrolled female,” to assign a speaker label to each identified segment. Speaker labels are identified for each speech segment by comparing the segment utterances to the enrolled speaker database and finding the “closest” speaker, if any. A speech segment having an unknown speaker is initially assigned a general speaker label from the set of background models. The “unenrolled” segment is assigned a segment number and receives a cluster identifier assigned by the clustering system. If a given segment is assigned a temporary speaker label associated with an unenrolled speaker, the user can be prompted by the present invention to identify the speaker. Once the user assigns a speaker label to an audio segment having an unknown speaker, the same speaker name can be automatically assigned to any segments that are assigned to the same cluster and the enrolled speaker database can be automatically updated to enroll the previously unknown speaker.

This application is a continuation-in-part of U.S. patent application Ser. No. 09/345,237, filed Jun. 30, 1999, which is a continuation-in-part of U.S. patent application Ser. No. 09/288,724, filed Apr. 9, 1999, issued as U.S. Pat. No. 6,345,252 each assigned to the assignee of the present invention and incorporated by reference herein.

FIELD OF THE INVENTION

The present invention relates generally to audio information classification systems and, more particularly, to methods and apparatus for transcribing audio information and identifying speakers in an audio file.

BACKGROUND OF THE INVENTION

Many organizations, such as broadcast news organizations and information retrieval services, must process large amounts of audio information, for storage and retrieval purposes. Frequently, the audio information must be classified by subject or speaker name, or both. In order to classify audio information by subject, a speech recognition system initially transcribes the audio information into text for automated classification or indexing. Thereafter, the index can be used to perform query-document matching to return relevant documents to the user.

Thus, the process of classifying audio information by subject has essentially become fully automated. The process of classifying audio information by speaker, however, often remains a labor intensive task, especially for real-time applications, such as broadcast news. While a number of computationally-intensive off-line techniques have been proposed for automatically identifying a speaker from an audio source using speaker enrollment information, the speaker classification process is most often performed by a human operator who identifies each speaker change, and provides a corresponding speaker identification.

The parent and grandparent applications to the present invention disclose methods and apparatus for retrieving audio information based on the audio content (subject) as well as the identity of the speaker. The parent application, U.S. patent application Ser. No. 09/345,237, for example, discloses a method and apparatus for automatically transcribing audio information from an audio source while concurrently identifying speakers in real-time, using an existing enrolled speaker database. The parent application, however, can only identify the set of the speakers in the enrolled speaker database. In addition, the parent application does not allow new speakers to be added to the enrolled speaker database while audio information is being processed in real-time. A need therefore exists for a method and apparatus that automatically identifies unknown speakers in real-time or in an off-line manner. A further need exists for a method and apparatus that automatically identifies unknown speakers using concurrent transcription, segmentation, speaker identification and clustering techniques.

SUMMARY OF THE INVENTION

Generally, a method and apparatus are disclosed for identifying speakers participating in an audio-video source, whether or not such speakers have been previously registered or enrolled. The disclosed unknown speaker classification system includes a speech recognition system, a speaker segmentation system, a clustering system and a speaker identification system. The speech recognition system produces transcripts with time-alignments for each word in the transcript. The speaker segmentation system separates the speakers and identifies all possible frames where there is a segment boundary between non-homogeneous speech portions. The clustering system clusters homogeneous segments (generally corresponding to the same speaker), and assigns a cluster identifier to each detected segment, whether or not the actual name of the speaker is known. Thus, segments corresponding to the same speaker should have the same cluster identifier.

According to one aspect of the invention, the disclosed speaker identification system uses an enrolled speaker database that includes background models for unenrolled speakers to assign a speaker to each identified segment. Once the speech segments are identified by the segmentation system, the disclosed unknown speaker identification system compares the segment utterances to the enrolled speaker database and finds the “closest” speaker, if any, to assign a speaker label to each identified segment. A speech segment having an unknown speaker is initially assigned a general speaker label from a set of background models for speaker identification, such as “unenrolled male” or “unenrolled female.” The “unenrolled” segment is assigned a segment number and receives a cluster identifier assigned by the clustering system. Thus, the clustering system assigns a unique cluster identifier for each speaker to further differentiate the general speaker labels.

The results of the present invention can be directly output to a user, for example, providing the transcribed text for each segment, together with the assigned speaker label. If a given segment is assigned a temporary speaker label associated with an unenrolled speaker, the user can be prompted to provide the name of the speaker. Once the user assigns a speaker label to an audio segment having an unknown speaker, the same speaker name can be automatically assigned to any segments having the same cluster identifier. In addition, the enrolled speaker database can be updated to enroll the previously unknown speaker using segments associated with the speaker as speaker training files.

A more complete understanding of the present invention, as well as further features and advantages of the present invention, will be obtained by reference to the following detailed description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an unknown speaker classification system according to the present invention;

FIG. 2 is a table from the time-stamped word database of FIG. 1;

FIG. 3 is a table from the speaker turn database of FIG. 1;

FIG. 4 illustrates a representative speaker enrollment process in accordance with the present invention;

FIG. 5 is a flow chart describing an exemplary unknown speaker identification process performed by the unknown speaker classification system of FIG. 1;

FIG. 6 is a flow chart describing an exemplary segmentation process performed by the unknown speaker classification system of FIG. 1;

FIG. 7 is a flow chart describing an exemplary speaker identification process performed by the unknown speaker classification system of FIG. 1; and

FIG. 8 is a flow chart describing an exemplary clustering process performed by the unknown speaker classification system of FIG. 1.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

FIG. 1 illustrates an unknown speaker classification system 100 in accordance with the present invention that automatically transcribes audio information from an audio-video source and automatically identifies speakers participating in the audio-video source, whether or not such speakers have been previously registered or enrolled. The audio-video source file may be, for example, an audio recording or live feed, for example, from a broadcast news program. The audio-video source is initially transcribed and concurrently processed to identify all possible frames where there is a segment boundary, indicating a speaker change.

The unknown speaker classification system 100 includes a speech recognition system, speaker segmentation system, a speaker identification system and a clustering system. The speech recognition system produces transcripts with time-alignments for each word in the transcript. The speaker segmentation system separates the speakers and identifies all possible frames where there is a segment boundary. A segment is a continuous portion of the audio source associated with a given speaker. The speaker identification system thereafter automatically assigns a speaker label to each segment associated with a known speaker or prompts the user for the identity of an unknown speaker. The clustering system clusters homogeneous segments, and assigns a cluster identifier to each detected segment, whether or not the actual name of the speaker is known. Thus, segments corresponding to the same speaker should have the same cluster identifier.

In accordance with one feature of the present invention, the user may be prompted for the identity of an unknown speaker associated with a given audio segment in real-time as the audio is presented, or the user may be prompted for the identity of an unknown speaker associated with a given cluster (collection of audio segments with sufficiently similar characteristics) in an off-line manner. In addition, the speaker name provided by the user can be used to update the enrolled speaker database. In one implementation of the present invention, a user interface presents the user with the current cluster number, and prompts the user to enter the name of the speaker.

As discussed further below, once the user assigns a speaker label to an audio segment having an unknown speaker in a real-time implementation, the present invention will automatically assign the same speaker name to any subsequent segments (as well as previously unassigned segments) that are assigned to the same cluster. Likewise, once the user assigns a speaker label to a cluster having an unknown speaker in an off-line implementation, the present invention will automatically assign the speaker name to all segments associated with the cluster.

According to one feature of the present invention, a segment having an unknown speaker is initially assigned a general speaker label from a set of background models for speaker identification, such as “unenrolled male” or “unenrolled female.” In the illustrative implementation, voice samples from several speakers are used to generate one collective model. Thus, the background models are the minimum required speaker names in the enrolled speaker database. When an “unenrolled speaker” label is produced by the speaker identification system, then the segment belongs to a speaker without a distinct identity. The “unenrolled” segment is assigned a segment number and receives a cluster identifier. Thus, according to another feature of the present invention, the speaker clustering module sub-classifies the unknown speaker into his or her own unique cluster. The clustering scheme automatically stores the audio file in terms of groups of segments associated with a given cluster. For example, each speech segment can have an associated cluster number. Thus, the clustering module serves to further differentiate each of the speakers that have been assigned the general label from the background model.

FIG. 1 is a block diagram showing the architecture of an illustrative unknown speaker classification system 100 in accordance with the present invention. The unknown speaker classification system 100 maybe embodied as a general purpose computing system, such as the general purpose computing system shown in FIG. 1. The unknown speaker classification system 100 includes a processor 110 and related memory, such as a data storage device 120, which may be distributed or local. The processor 110 may be embodied as a single processor, or a number of local or distributed processors operating in parallel.

The data storage device 120 and/or a read only memory (ROM) are operable to store one or more instructions, which the processor 110 is operable to retrieve, interpret and execute.

The data storage device 120 preferably includes an audio corpus database 150 for storing one or more prerecorded or live audio or video files (or both) that can be processed in real-time in accordance with the present invention. The data storage device 120 also includes a time-stamped word database 200, discussed further below in conjunction with FIG. 2, that is optionally produced by the speech recognition system and includes a set of time-stamped words. In addition, a speaker turn database 300, discussed further below in conjunction with FIG. 3, is produced by the speaker identification system, in conjunction with the speaker segmentation system, and indicates the start time of each segment, together with one or more corresponding suggested speaker labels (including labels from the selection of background models, such as “unenrolled female”), as well as a cluster identifier. The speaker database 420 is produced by a speaker enrollment process 410, discussed below in conjunction with FIG. 4, and includes an entry for each enrolled speaker. In addition, in accordance with the present invention, the enrolled speaker database 420 includes the background models for unenrolled speakers. It is noted that the generated databases 200 and 300 shown in the illustrative embodiment of FIG. 1 may not be required for an online implementation where the results of the present invention are displayed to a user in real-time, and are not required for subsequent access.

In addition, as discussed further below in conjunction with FIGS. 5 through 8, the data storage device 120 includes an unknown speaker identification process 500, a transcription engine 515, a segmentation process 600, a speaker identification process 700 and a clustering process 800. The unknown speaker identification process 500 coordinates the execution of the transcription engine 515, segmentation process 600, speaker identification process 700 and clustering process 800. The unknown speaker identification process 500 analyzes one or more audio files in the audio corpus database 150 and produces a transcription of the audio information in real-time, that indicates the speaker associated with each segment. In the event a background model label is assigned to a given speech segment, the unknown speaker identification process 500 will prompt the user for the name of the corresponding speaker. The segmentation process 600 separates the speakers and identifies all possible frames where there is a segment boundary. The speaker identification process 700 assigns a speaker label to each segment using an enrolled speaker database that has been supplemented with background models in accordance with the present invention. The clustering process 800 merges previously unclustered segments or previously clustered clusters into a new set of clusters.

FIG. 2 illustrates an exemplary time-stamped word database 200 that is produced by the speech recognition system and includes a set of time-stamped words. The time-stamped word database 200 maintains a plurality of records, such as records 211 through 214, each associated with a different word in the illustrative embodiment. For each word identified in field 220, the time-stamped word database 200 indicates the start time of the word in field 230.

FIG. 3 illustrates an exemplary speaker turn database 300 that is produced by the speaker identification system, in conjunction with the speaker segmentation system, and indicates the start time of each segment, together with one or more corresponding suggested speaker labels (including labels from the selection of background models, such as “unenrolled female”), as well as a cluster identifier. The speaker turn database 300 maintains a plurality of records, such as records 304 through 308, each associated with a different segment in the illustrative embodiment. For each segment identified by a segment number in field 320, the speaker turn database 300 indicates the start time of the segment in field 330, relative to the start time of the audio source file. In addition, the speaker turn database 300 identifies the speaker (including labels from the selection of background models, such as “unenrolled female”) associated with each segment in field 340, together with the corresponding speaker score in field 350. Finally, the speaker turn database 300 identifies the cluster identifier that has been assigned to the segment in accordance with the BIC criterion, in a manner discussed further below.

SPEAKER REGISTRATION PROCESS

FIG. 4 illustrates a known process used to register or enroll speakers. As previously indicated, the present invention supplements the enrolled speaker information with background models for unenrolled speakers. As shown in FIG. 4, for each registered speaker, the name of the speaker is provided to a speaker enrollment process 410, together with a speaker training file, such as a pulse-code modulated (PCM) file. The speaker enrollment process 410 analyzes the speaker training file, and creates an entry for each speaker in a speaker database 420. As previously indicated, the speaker database 420 includes models for unenrolled speakers, such as “unenrolled male” and “unenrolled female.” The process of adding speaker's voice samples to the speaker database 420 is called enrollment. The enrollment process is off line and the speaker identification system assumes such a database exists for all speakers of interest. About a minute's worth of audio is generally required from each speaker from multiple channels and microphones encompassing multiple acoustic conditions. The training data or database of enrolled and unenrolled speakers is stored using a hierarchical structure so that accessing the models is optimized for efficient recognition and retrieval.

PROCESSES

As previously indicated, the unknown speaker identification process 500, shown in FIG. 5, coordinates the execution of the transcription engine 515, segmentation process 600 (FIG. 6), speaker identification process 700 (FIG. 7) and clustering process 800 (FIG. 8). The unknown speaker identification process 500 analyzes one or more audio files in the audio corpus database 150 and produces a transcription of the audio information in real-time, that indicates the name of the speaker associated with each segment, if available, or prompts the user to assign a speaker name to an unknown speaker.

In one implementation, shown in FIG. 5, the segmentation process 600 (FIG. 6) and clustering process 800 (FIG. 8) are performed using a single processing thread. Once the speech segments are identified by the segmentation process 600, the speaker identification process 700 compares the segment utterances to the enrolled speaker database and finds the “closest” speaker, if any, to assign a speaker label to each identified segment. A speech segment having an unknown speaker is initially assigned a general speaker label from a set of background models for speaker identification, such as “unenrolled male” or “unenrolled female.” The “unenrolled” segment is assigned a segment number and receives a cluster identifier assigned by the clustering process 800. Thus, the clustering process 800 assigns a unique cluster identifier for each speaker to further differentiate the general speaker labels.

As shown in FIG. 5, the unknown speaker identification process 500 initially extracts cepstral features from the audio files during step 510, in a known manner. Generally, step 510 changes the domain of the audio signal from a temporal domain to the frequency domain, analyzes the signal energy in various frequency bands, and applies another transform to change the domain of the signal to the cepstral domain.

As shown in FIG. 5, step 510 provides common front-end processing for the transcription engine 515, segmentation process 600 (FIG. 6) and speaker identification process 700 (FIG. 7). Generally, the feature vectors computed during step 510 can be distributed to the three multiple processing threads corresponding to the transcription engine 515, segmentation process 600 (FIG. 6) and speaker identification process 700 (FIG. 7). The feature vectors can be distributed to the three multiple processing threads, for example, using a shared memory architecture that acts in a server-like manner to distribute the computed feature vectors to each channel (corresponding to each processing thread).

The feature vectors generated during step 510 are processed along parallel branches in a multi-threaded environment. As shown in FIG. 5 and discussed hereinafter, the generated feature vectors are applied using multiple threads to (i) a transcription engine during step 515, (i) the speaker segmentation process 600, discussed below in conjunction with FIG. 6, during step 530; and (iii) the speaker identification process 700, discussed below in conjunction with FIG. 7, during step 560.

The generated feature vectors are applied during step 515 to a transcription engine, such as the ViaVoice™ speech recognition system, commercially available from IBM Corporation of Armonk, N.Y., to produce a transcribed file of time-stamped words. Thereafter, the time-stamped words can optionally be collected into a time-stamped word database 200 during step 520. In addition, the time-stamped words are applied to an interleaver during step 540, discussed below.

The generated feature vectors are applied during step 530 to the segmentation process 600, discussed further below in conjunction with FIG. 6. Generally, the segmentation process 600 separates the speakers and identifies all possible frames where there is a segment boundary between non-homogeneous speech portions. Each frame where there is a segment boundary is referred to as a turn and each homogeneous segment should correspond to the speech of a single speaker. Once delineated by the segmentation process 600, each segment can be classified as having been spoken by a particular speaker (assuming the segment meets the minimum segment length requirement required for the speaker recognition system).

The turns identified by the segmentation process 600, together with the feature vectors generated during step 510, are then applied to a speaker identification process 700, discussed further below in conjunction with FIG. 7, during step 560 to assign a speaker label (including labels from the background models) to each segment using the enrolled speaker database 420. Generally, the speaker identification system compares the segment utterances to the speaker database 420 (FIG. 4) and finds the “closest” speaker, which may be an “unenrolled speaker.” The assigned speaker labels produced by the speaker identification process 700 are applied to stage 550, discussed below.

The speaker turns identified by the segmentation process 600 during step 530 are likewise applied to a clustering process 800, discussed further below in conjunction with FIG. 8, during step 532 to merge the speaker turns with previously identified clusters or to create a new cluster, as appropriate. The clustering process 800 will assign a cluster identifier to each segment, which may optionally be recorded in field 360 of the speaker turn database 300.

The time-stamped words produced by the transcription engine during step 515, together with the speaker turns identified by the segmentation process 600 during step 530 are applied to an interleaver during step 540 to interleave the turns with the time-stamped words and produce isolated speech segments. The isolated speech segments and speaker identifications produced by the speaker identification system during step 560 are then displayed to the user during step 550.

In one implementation, the isolated speech segments are displayed in real-time as they are produced by the interleaver during step 540. In addition, in the illustrative embodiment, the minimum segment length required for the speaker recognition system is eight seconds. Thus, the speaker identification labels will generally be appended to the transcribed text approximately eight seconds after the beginning of the isolated speech segment is first presented. It is noted that if the isolated speech segment is shorter than the minimum segment length required for the speaker recognition system, then a speaker label such as “inconclusive” can be assigned to the segment.

As shown in FIG. 5, the user may be prompted for the identity of an unknown speaker associated with a given audio segment during step 580. In a real-time implementation, the user can be prompted as the speaker identification labels are appended to the transcribed text. In an off-line implementation, the user can be prompted for the identity of an unknown speaker associated with a given cluster, once the complete audio file has been played. For example, the user can select a segment from among the segments in each cluster and thereafter indicate the corresponding speaker.

The speaker names provided by the user during step 580 can be used to update the speaker turn database 300 and the enrolled speaker database 420. In this manner, once the user assigns a speaker label to an audio segment having an unknown speaker in a real-time implementation, the same speaker name is automatically assigned to any segments that are assigned to the same cluster. Likewise, once the user assigns a speaker label to a cluster having an unknown speaker in an off-line implementation, the present invention will automatically assign the speaker name to all segments associated with the cluster.

The enrolled speaker database 420 is updated with a newly identified speaker by collecting the segments associated with the given speaker and using the segments as speaker training files, in the manner discussed above in conjunction with FIG. 5.

Bayesian Information Criterion (BIC) Background

As previously indicated, the segmentation process 600, shown in FIG. 6, separates the speakers and identifies all possible frames where there is a segment boundary between non-homogeneous speech portions. Each frame where there is a segment boundary is referred to as a turn and each homogeneous segment should correspond to the speech of a single speaker. Once delineated by the segmentation process 600, each segment can be classified as having been spoken by a particular speaker (assuming the segment meets the minimum segment length requirement required for speaker recognition system).

The segmentation process 600 and clustering process 800 are based on the Bayesian Information Criterion (BIC) model-selection criterion. BIC is an asymptotically optimal Bayesian model-selection criterion used to decide which of p parametric models best represents n data samples x_(l), . . . x_(n), x_(i) εR^(d). Each model M_(j) has a number of parameters, k_(j). The samples x_(i) are assumed to be independent.

For a detailed discussion of the BIC theory, see, for example, G. Schwarz, “Estimating the Dimension of a Model,” The Annals of Statistics, Vol. 6, 461-464 (1978), incorporated by reference herein. According to the BIC theory, for sufficiently large n, the best model of the data is the one which maximizes the following expression:

BIC_(j)=log L _(j)(x _(l) , . . . , x _(n))−½λk _(j) log n  Eq.(1)

where λ=1, and where L_(j) is the maximum likelihood of the data under model M_(j) (in other words, the likelihood of the data with maximum likelihood values for the k_(j) parameters of M_(j)). When there are only two models, a simple test is used for model selection. Specifically, the model M₁ is selected over the model M₂ ifΔBIC=BIC₁−BIC₂, is positive. Likewise, the model M₂ is selected over the model M₁ ifΔBIC=BIC₁−BIC₂, is negative.

Speaker Segmentation

The segmentation process 600, shown in FIG. 6, identifies all possible frames where there is a segment boundary. Without loss of generality, consider a window of consecutive data samples (x_(l), . . . x_(n)) in which there is at most one segment boundary.

The basic question of whether or not there is a segment boundary at frame i can be cast as a model selection problem between the following two models: model M₁, where (x_(l), . . . x_(n)) is drawn from a single full-covariance Gaussian, and model M₂, where (x_(l), . . . x_(n)) is drawn from two full-covariance Gaussians, with (x_(l), . . . x_(i)) drawn from the first Gaussian, and (x_(i+l), . . . x_(n)) drawn from the second Gaussian.

Since x₁ ∈ R^(d), model M₁ has $\quad {{k_{1} = {d + {\frac{d\left( {d + 1} \right)}{2}\quad {parameters}}}},}\quad$

while model M₂ has twice as many parameters (k₂=2k₁). It can be shown that the i^(th) frame is a good candidate for a segment boundary if the expression: $\begin{matrix} {{{{\Delta \quad {BIC}_{i}} = \quad \left. {{{- \frac{n}{2}}\log {{\sum_{w}\left| {{+ \frac{i}{2}}\log} \middle| \sum_{f} \right.}}} + {\frac{n - i}{2}\log}} \middle| \sum_{s} \right.}} +} \\ {\quad {\frac{1}{2}{\lambda \left( {d + \frac{d\left( {d + 1} \right)}{2}} \right)}\log \quad n}} \end{matrix}$

is negative, where |Σ_(w)| is the determinant of the covariance of the whole window (i.e., all n frames), |Σ_(f)| is the determinant of the covariance of the first subdivision of the window, and |Σ_(s)| is the determinant of the covariance of the second subdivision of the window.

Thus, two subsamples, (x_(l), . . . x_(i)) and (x_(i+l), . . . x_(n)), are established during step 610 from the window of consecutive data samples (x_(l), . . . x_(n)). The segmentation process 600 performs a number of tests during steps 615 through 628 to eliminate some BIC tests in the window, when they correspond to locations where the detection of a boundary is very unlikely. Specifically, the value of a variable α is initialized during step 615 to a value of n/r−1, where r is the detection resolution (in frames). Thereafter, a test is performed during step 620 to determine if the value a exceeds a maximum value, α_(max). If it is determined during step 620 that the value α exceeds a maximum value, α_(max), then the counter i is set to a value of (α-α_(max)+1)r during step 624. If, however, it is determined during step 620 that the value α does not exceed a maximum value, α_(max), then the counter i is set to a value of r during step 628. Thereafter, the difference in BIC values is calculated during step 630 using the equation set forth above.

A test is performed during step 640 to determine if the value of i equals n-r. In other words, have all possible samples in the window been evaluated. If it is determined during step 640 that the value of i does not yet equal n-r, then the value of i is incremented by r during step 650 to continue processing for the next sample in the window at step 630. If, however, it is determined during step 640 that the value of i equals n-r, then a further test is performed during step 660 to determine if the smallest difference in BIC values (αBIC_(i0)) is negative. If it is determined during step 660 that the smallest difference in BIC values is not negative, then the window size is increased during step 665 before returning to step 610 to consider a new window in the manner described above. Thus, the window size, n, is only increased when the ΔBIC values for all i in one window have been computed and none of them leads to a negative ΔBIC value.

If, however, it is determined during step 660 that the smallest difference in BIC values is negative, then i₀ is selected as a segment boundary during step 670. Thereafter, the beginning of the new window is moved to i₀+1 and the window size is set to N₀ during step 675, before program control returns to step 610 to consider the new window in the manner described above.

Thus, the BIC difference test is applied for all possible values of i, and i₀ is selected with the most negative ΔBIC_(i). A segment boundary can be detected in the window at frame i: if ΔBIC_(i0)>0, then x_(i0) corresponds to a segment boundary. If the test fails then more data samples are added to the current window (by increasing the parameter n) during step 660, in a manner described below, and the process is repeated with this new window of data samples until all the feature vectors have been segmented. Generally, the window size is extended by a number of feature vectors, which itself increases from one window extension to another. However, a window is never extended by a number of feature vectors larger than some maximum value. When a segment boundary is found during step 670, the window extension value retrieves its minimal value (N₀).

Variable Window Scheme

According to a further feature of the present invention, a new window selection scheme is presented that improves the overall accuracy, especially on small segments. The choice of the window size on which the segmentation process 600 is performed is very important. If the selected window contains too many vectors, some boundaries are likely to be missed. If, on the other hand, the selected window is too small, lack of information will result in poor representation of the data by the Gaussians.

It has been suggested to add a fixed amount of data to the current window if no segment boundary has been found. Such a scheme does not take advantage of the ‘contextual’ information to improve the accuracy: the same amount of data is added, whether or not a segment boundary has just been found, or no boundary has been found for a long time.

The improved segmentation process of the present invention considers a relatively small amount of data in areas where new boundaries are very likely to occur, and increases the window size more generously when boundaries are not very likely to occur. Initially, a window of vectors of a small size is considered (typically 100 frames of speech). If no segment boundary is found on the current window, the size of the window is increased by ΔN_(i) frames. If no boundary is found in this new window, the number of frames is increased by ΔN_(i+l), with ΔN_(i)=ΔN_(i+l)+δ_(i), where δ_(i)=2δ_(i+l), until a segment boundary is found or the window extension has reached a maximum size (in order to avoid accuracy problems if a boundary occurs). This ensures an increasing window size which is pretty slow when the window is still small, and is faster when the window gets bigger. When a segment boundary is found in a window, the next window begins after the detected boundary, using the minimal window size.

Improving Efficiency of BIC Tests

According to another feature of the present invention, improvements in the overall processing time are obtained by better selection of the locations where BIC tests are performed. Some of the BIC tests in the window can be arbitrarily eliminated, when they correspond to locations where the detection of a boundary is very unlikely. First, the BIC tests are not performed at the borders of each window, since they necessarily represent one Gaussian with very little data (this apparently small gain is repeated over segment detections and actually has no negligible performance impact).

In addition, when the current window is large, if all the BIC tests are performed, the BIC computations at the beginning of the window will have been done several times, with some new information added each time. If no segment boundary has been found in the first 5 seconds, for example, in a window size of 10 seconds, it is quite unlikely that a boundary will be hypothesized in the first 5 seconds with an extension of the current 10 second window. Thus, the number of BIC computations can be decreased by ignoring BIC computations in the beginning of the current window (following a window extension). In fact, the maximum number of BIC computations is now an adjustable parameter, tweaked according to the speed/accuracy level required (α_(max) in FIG. 3).

Thus, the segmentation process 600 permits knowledge of the maximum time it takes before having some feedback on the segmentation information. Because even if no segment boundary has been found yet, if the window is big enough one knows that there is no segment present in the first frames. This information can be used to do other processing on this part of the speech signal.

BIC Penalty Weight

The BIC formula utilizes a penalty weight parameter, λ, in order to compensate for the differences between the theory and the practical application of the criterion. It has been found that the best value of λ that gives a good tradeoff between miss rate and false-alarm rate is 1.3. For a more comprehensive study of the effect of λ on the segmentation accuracy for the transcription of broadcast news, see, A. Tritschler, “A Segmentation-Enabled Speech Recognition Application Using the BIC,” M.S. Thesis, Institut Eurecom (France, 1998), incorporated by reference herein.

While in principle the factor λ is task-dependent and has to be retuned for every new task, in practice the algorithm has been applied to different types of data and there is no appreciable change in performance by using the same value of λ.

Speaker Identification Process

As previously indicated, the unknown speaker identification process 500 executes a speaker identification process 700, shown in FIG. 7, during step 560 to assign a speaker label to each segment using the enrolled speaker database 420. As shown in FIG. 7, the speaker identification process 700 receives the turns identified by the segmentation process 600, together with the feature vectors generated by the common front-end processor during step 510. Generally, the speaker identification system compares the segment utterances to the speaker database 420 (FIG. 4) and finds the “closest” speaker.

The turns and feature vectors are processed during step 710 to form segment utterances, comprised of chunks of speech by a single speaker. The segment utterances are applied during step 720 to a speaker identification system. For a discussion of a speaker identification system, see, for example, H. S. M. Beigi et al., “IBM Model-Based and Frame-By-Frame Speaker-Recognition,” in Proc. of Speaker Recognition and Its Commercial and Forensic Applications, Avignon, France (1998). Generally, the speaker identification system compares the segment utterances to the speaker database 420 (FIG. 4) and finds the “closest” speaker.

The speaker identification system has two different implementations, a model-based approach and a frame-based approach with concomitant merits and demerits. The engine is both text and language independent to facilitate live audio indexing of material such as broadcast news.

Speaker Identification—The Model-Based Approach

To create a set of training models for the population of speakers in the database, a model M_(i) for the i^(th) speaker based on a sequence of M frames of speech, with the d-dimensional feature vector {{right arrow over (f)}_(m)}_(m=l . . . M), is computed. These models are stored in terms of their statistical parameters, such as, {{right arrow over (μ)}_(i,j), Σ_(i,j), {right arrow over (C)}_(i,j)}_(j=l, . . . , n) _(i) , consisting of the Mean vector, the Covariance matrix, and the Counts, for the case when a Gaussian distribution is selected. Each speaker, i, may end up with a model consisting of n_(i) distributions.

Using the distance measure proposed in H. S. M. Beigi et. al, A Distance Measure Between Collections of Distributions and Its Application to Speaker Recognition,′ Proc. ICASSP98}, Seattle, Wash., 1998, for comparing two such models, a hierarchical structure is created to devise a speaker recognition system with many different capabilities including speaker identification (attest a claim), speaker classification (assigning a speaker), speaker verification (second pass to confirm classification by comparing label with a “cohort” set of speakers whose characteristics match those of the labeled speaker), and speaker clustering.

The distance measure devised for speaker recognition permits computation of an acceptable distance between two models with a different number of distributions n_(i). Comparing two speakers solely based on the parametric representation of their models obviates the need to carry the features around making the task of comparing two speakers much less computationally intensive. A short-coming of this distance measure for the recognition stage, however, is that the entire speech segment has to be used to build the model of the test individual (claimant) before computation of the comparison can begin. The frame-by-frame approach alleviates this problem.

Speaker Identification—The Frame-By-Frame Approach

Let M_(i) be the model corresponding to the i^(th) enrolled speaker. M_(i) is entirely defined by the parameter set, {{right arrow over (μ)}_(i,j),Σ_(i,j), {right arrow over (p)}_(i,j)}_(j=l, . . . , n) _(i) , consisting of the mean vector, covariance matrix, and mixture weight for each of the n_(i) components of speaker i's Gaussian Mixture Model (GMM). These models are created using training data consisting of a sequence of M frames of speech, with the d-dimensional feature vector, {{right arrow over (f)}_(in)}_(m=l, . . . , M), as described in the previous section. If the size of the speaker population is N_(p), then the set of the model universe is {M_(i)}_(i=l, . . . , N) _(p) . The fundamental goal is to find the i such that M_(i) best explains the test data, represented as a sequence of N frames, {{right arrow over (f)}_(n)}_(n=l, . . . , N), or to make a decision that none of the models describes the data adequately. The following frame-based weighted likelihood distance measure, d_(i,n), is used in making the decision: d_(i,n)=−log[Σ_(j=l) ^(ni)p_(i,j)p(f_(n)|j^(th) com{right arrow over (po)}nent of M_(i))],

where, using a Normal representation, $\left. {{{p\left( {\overset{\rightarrow}{f}}_{n} \right.}} \cdot} \right) = {\frac{1}{\left( {2\quad \pi} \right)^{d/2}{\sum_{i,j}}^{1/2}}e^{{- \frac{1}{2}}{({{\overset{\rightarrow}{f}}_{n} - {\overset{\rightarrow}{\mu}}_{i,j}})}^{t}{\sum\limits_{i,j}^{- 1}{({{\overset{\rightarrow}{f}}_{n} - {\overset{\rightarrow}{\mu}}_{i,j}})}}}}$

The total distance, D_(i), of model M_(i) from the test data is then taken to be the sum of all the distances over the total number of test frames.

For classification, the model with the smallest distance to that of the speech segment is chosen. By comparing the smallest distance to that of a background model, one could provide a method to indicate that none of the original models match very well. Alternatively, a voting technique may be used for computing the total distance.

For verification, a predetermined set of members that form the cohort of the labeled speaker is augmented with a variety of background models. Using this set as the model universe, the test data is verified by testing if the claimant's model has the smallest distance; otherwise, it is rejected.

This distance measure is not used in training since the frames of speech would have to retained for computing the distances between the speakers. The training is done, therefore, using the method for the model-based technique discussed above.

The assigned speaker label generated during step 720 can optionally be provisionally provided to the block 550 (FIG. 5) for output to the user, in a manner described below. The assigned speaker label is verified during step 730 by taking a second pass over the results of speaker classification. If the speaker identification is verified during step 730, then the speaker label is provided to the block 550 (FIG. 5) for output to the user. In addition, an entry can optionally be created in the speaker turn database 300 during step 740 indicating the best choice, together with the assigned score indicating the distance from the original enrolled speaker model to the audio test segment, and alternative choices, if desired.

SPEAKER CLUSTERING

BIC Processing for Clustering

The clustering process 800 attempts to merge one of a set of clusters C₁, . . . , C_(K), with another cluster, leading to a new set of clusters C₁′, . . . , C_(K−1)′, where one of the new clusters is the merge between two previous clusters. To determine whether to merge two clusters, C_(i) and C_(j), two models are built: the first model, M₁, is a Gaussian model computed with the data of C_(i) and C_(j) merged which leads to BIC₁. The second model, M₂, keeps two different Gaussians, one for C_(i) and one for C_(j), and gives BIC₂. Thus, it is better to keep two distinct models if ΔBIC=BIC₁−BIC₂ <0. If this difference of BIC is positive, the two clusters are merged and we have the desired new set of clusters.

S. Chen and P. Gopalakrishnan, “Speaker, Environment and Channel Change Detection and Clustering Via the Bayesian Information Criterion,” Proceedings of the DARPA Workshop, 1998, incorporated by reference herein, shows how to implement off-line clustering in a bottom-up fashion, i.e., starting with all the initial segments, and building a tree of clusters by merging the closest nodes of the tree (the measure of similarity being the BIC). The clustering process 800 implements a new online technique.

As discussed below in conjunction with FIG. 8, the online clustering of the present invention involves first the K clusters found in the previous iterations (or calls to the clustering procedure 800), and the new M segments to cluster.

Clustering Subroutine

As previously indicated, the speaker classification process 200 implements the clustering process 800 (FIG. 8) during step 230 to cluster homogenous segments that were identified by the segmentation subroutine 300 (whether or not the identity of the speaker is actually known). The identified segments are clustered with another identified segment or a cluster identified during a previous iteration of the clustering process 800.

As shown in FIG. 8, the clustering process 800 initially collects the M new segments to be clustered and the K existing clusters during step 810. For all unclustered segments, the clustering process 800 calculates the difference in BIC values relative to all of the other M−1 unclustered segments during step 820, as follows: $\begin{matrix} {{{{\Delta \quad {BIC}_{i}} = \quad \left. {{{- \frac{n}{2}}\log {{\sum_{w}\left| {{+ \frac{i}{2}}\log} \middle| \sum_{f} \right.}}} + {\frac{n - i}{2}\log}} \middle| \sum_{s} \right.}} +} \\ {\quad {\frac{1}{2}{\lambda \left( {d + \frac{d\left( {d + 1} \right)}{2}} \right)}\log \quad n}} \end{matrix}$

In addition, for all unclustered segments, the clustering process 800 also calculates the difference in BIC values relative to the K existing clusters during step 830, as follows: $\begin{matrix} {{{{\Delta \quad {BIC}_{i}} = \quad \left. {{{- \frac{n}{2}}\log {{\sum_{w}\left| {{+ \frac{i}{2}}\log} \middle| \sum_{f} \right.}}} + {\frac{n - i}{2}\log}} \middle| \sum_{s} \right.}} +} \\ {\quad {\frac{1}{2}{\lambda \left( {d + \frac{d\left( {d + 1} \right)}{2}} \right)}\log \quad n}} \end{matrix}$

Thereafter, the clustering process 800 identifies the largest difference in BIC values, ΔBIC_(max), among the M(M+K−1) results during step 840. A test is then performed during step 850 to determine if the largest difference in BIC values, ΔBIC_(max), is positive. As discussed further below, the ΔBIC_(max) value is the largest difference of BICs among all possible combinations of existing clusters and new segments to be clustered. It is not only the largest difference given the current new segment, which would take each segment in series and attempt to merge the segment with a cluster or create a new cluster, but rather the clustering process 800 implements an optimal approach given all the new segments.

If it is determined during step 850 that the largest difference in BIC values, ΔBIC_(max), is positive, then the current segment is merged with the existing cluster and the value of M is incremented, or the new segment is merged with another unclustered segment and the value of K is incremented, and the value of M is decremented by two, during step 860. Thus, the counters are updated based on whether there are two segments and a new cluster has to be created (M=M−2 and K=K+1), because the two segments correspond to the same cluster, or if one of the entities is already a cluster, then the new segment is merged into the cluster (M=M−1 and K is constant). Thereafter, program control proceeds to step 880, discussed below.

If, however, it is determined during step 850 that the largest difference in BIC values, ΔBIC_(max), is not positive, then the current segment is identified as a new cluster, and either (i) the value of the cluster counter, K, is incremented and the value of the segment counter, M, is decremented, or (ii) the value of the cluster counter, K, is incremented by two and the value of the segment counter, M, is decremented by two, during step 870, based on the nature of the constituents of ΔBIC_(max). Thus, the counters are updated based on whether there is one segment and one existing cluster (M=M−1 and K=K+1) or two new segments (M=M−2 and K=K+2).

Thereafter, a test is performed during step 880 to determine if the value of the segment counter, M, is strictly positive, indicating that additional segments remain to be processed. If it is determined during step 880 that the value of the segment counter, M, is positive, then program control returns to step 840 to continue processing the additional segment(s) in the manner described above. If, however, it is determined during step 880 that the value of the segment counter, M, is zero, then program control terminates.

The clustering process 800 is a suboptimal algorithm in comparison to the off line bottom-up clustering technique discussed above, since the maxima considered for the ΔBIC values can be local in the off line scheme, as opposed to the global maxima found in the online version. Since the optimal segment merges are usually those corresponding to segments close in time, the online clustering process 800 makes it easier to associate such segments to the same cluster. In order to reduce the influence of the non-reliable small segments to cluster, only the segments with sufficient data are clustered; the other segments are gathered in a separate ‘garbage’ cluster. Indeed, the small segments can lead to errors in clustering, due to the fact that the Gaussians could be poorly represented. Therefore, in order to improve the classification accuracy, the small segments are all given a cluster identifier of zero, which means that no other clustering decision can be taken.

It is to be understood that the embodiments and variations shown and described herein are merely illustrative of the principles of this invention and that various modifications may be implemented by those skilled in the art without departing from the scope and spirit of the invention. 

What is claimed is:
 1. A method for identifying a speaker in an audio source, said method comprising the steps of: transcribing said audio source to create a textual version of said audio information; identifying potential segment boundaries in said audio source; and assigning a speaker label to each identified segment, said speaker label being selected from a speaker database that includes at least one model for an unenrolled speaker.
 2. The method of claim 1, wherein said assigning step further comprises the step of assigning a score indicating the confidence of said assigned speaker label.
 3. The method of claim 1, further comprising the step of prompting a user for a name of an unenrolled speaker.
 4. The method of claim 1, further comprising the step of clustering homogeneous segments into a cluster.
 5. The method of claim 4, further comprising the step of assigning an identity to all segments in the same cluster.
 6. The method of claim 1, wherein said assigning step utilizes an enrolled speaker database to assign a speaker label to each identified segment.
 7. The method of claim 4, further comprising the step of using segments in the same cluster as speaker training files to update an enrolled speaker database with said unenrolled speaker.
 8. A method for identifying a speaker in an audio source, said method comprising the steps of: computing feature vectors from said audio source; and applying said feature vectors to parallel processing branches to: transcribe said audio source to create a textual version of said audio information; identify potential segment boundaries in said audio source; and assign a speaker label to each identified segment, said speaker label being selected from a speaker database that includes at least one model for an unenrolled speaker.
 9. The method of claim 8, wherein said feature vectors are applied to said parallel branches using a shared memory architecture.
 10. The method of claim 9, wherein said shared memory architecture distributes the computed feature vectors to a channel corresponding to each of said parallel processing branches.
 11. A system for identifying a speaker in an audio source, comprising: a memory that stores computer-readable code; and a processor operatively coupled to said memory, said processor configured to implement said computer-readable code, said computer-readable code configured to: transcribe said audio source to create a textual version of said audio information; identify potential segment boundaries in said audio source substantially concurrently with said transcribing step; and assign a speaker label to each identified segment, said speaker label being selected from a speaker database that includes at least one model for an unenrolled speaker.
 12. An article of manufacture, comprising: a computer readable medium having computer readable code means embodied thereon, said computer readable program code means comprising: a step to transcribe said audio source to create a textual version of said audio information; a step to identify potential segment boundaries in said audio source substantially concurrently with said transcribing step; and a step to assign a speaker label to each identified segment, said speaker label being selected from a speaker database that includes at least one model for an unenrolled speaker.
 13. A method for identifying a speaker in an audio source, said method comprising the steps of: transcribing said audio source to create a textual version of said audio information; identifying potential segment boundaries in said audio source; assigning a speaker label to each identified segment, said speaker label being selected from a speaker database that includes at least one model for an unenrolled speaker; and presenting said textual version together with said assigned speaker labels.
 14. The method of claim 13, further comprising the step of prompting a user for a name of an unenrolled speaker.
 15. The method of claim 13, further comprising the step of clustering homogeneous segments into a cluster.
 16. The method of claim 15, further comprising the step of assigning an identity to all segments in the same cluster.
 17. The method of claim 13, wherein said assigning step utilizes an enrolled speaker database to assign a speaker label to each identified segment.
 18. The method of claim 15, further comprising the step of using segments in the same cluster as speaker training files to update an enrolled speaker database with said unenrolled speaker.
 19. A method for identifying a speaker in an audio source, said method comprising the steps of: assigning a speaker label to each segment in said audio source, said speaker label being selected from a speaker database that includes at least one model for an unenrolled speaker; presenting said user with said assigned speaker labels; and prompting a user for an identity of an unenrolled speaker.
 20. A system for identifying a speaker in an audio source, comprising: a memory that stores computer-readable code; and a processor operatively coupled to said memory, said processor configured to implement said computer-readable code, said computer-readable code configured to: assigning a speaker label to each segment in said audio source, said speaker label being selected from a speaker database that includes at least one model for an unenrolled speaker; presenting said user with said assigned speaker labels; and prompting a user for the identity of an unenrolled speaker.
 21. A method for identifying a speaker in an audio source, said method comprising the steps of: transcribing said audio source to create a textual version of said audio information; identifying potential segment boundaries in said audio source; clustering homogeneous segments into a cluster; and assigning a speaker label to each identified segment, said speaker label being selected from a speaker database that includes at least one model for an unenrolled speaker.
 22. The method of claim 21, further comprising the step of prompting a user for a name of an unenrolled speaker.
 23. The method of claim 22, further comprising the step of assigning said name to all segments in the same cluster.
 24. The method of claim 21, wherein said assigning step utilizes an enrolled speaker database to assign a speaker label to each identified segment.
 25. The method of claim 21, further comprising the step of using segments in the same cluster as speaker training files to update an enrolled speaker database with said unenrolled speaker. 